Large-Scale Phenotyping Inferences: From Trees to Forest through Machine Learning
presentation
posted on 2019-05-15, 04:30 authored by Maxime Bombrun, Jonathan Dash, Heidi Dungey, David Pont, Michael WattThe genomics revolution has provided rapid gains in crop productivity by shortening the breeding
cycle of many commercial species. Genomic data is most useful when carefully linked to crop
phenotypic expression and environmental conditions (Dungey, et al., 2018). Within forests, individual
tree phenotyping is exceptionally challenging due to their considerable size, the long breeding cycle,
and the high variability of growing conditions (Pont, 2016). Advances in remote sensing and the
emergence of sophisticated methods for large data analytics provide a means for describing and
analysing phenotypic and environmental variation at a forest scale.
This study outlines the development and implementation of a phenotyping system that provides
spatial estimates of stand productivity across a large plantation forest. Using a machine learning
method forest productivity was modelled from an extensive set of 18 million observations of 93
variables describing climate, forest management, genetics and terrain, extracted from environmental
surfaces, management records and LiDAR data (Watt, et al., 2013). The most important determinants
of productivity were the genetic information and seasonal air temperatures, followed by variables
describing the silvicultural treatment. The phenotyping method developed here can be used to
identify superior and inferior genotypes and estimate a productivity index for each site, which will
improve tree breeding and increase overall productivity across the forest.
References
Dungey, H. S., Dash, J. P., Pont, D., Clinton, P. W., Watt, M. S., & Telfer, E. J. (2018). Phenotyping Whole Forests Will Help to Track Genetic Performance. Trends in plant science.
Pont, D. (2016). Assessment of individual trees using aerial laser scanning in New Zealand radiata pine forests.
Watt, P., & Watt, M. S. (2013). Development of a national model of Pinus radiata stand volume from LiDAR metrics for New Zealand. International journal of remote sensing, 34(16), 5892-5904.
ABOUT THE AUTHOR
Maxime Bombrun is a data scientist in the Data Analytics team at Scion. He received a PhD in
image processing and geology from the University of Clermont-Ferrand, France, in 2015, and
completed a two-year post-doctoral fellowship in biomedicine at the University of Uppsala, Sweden.
His research interests include image processing, statistical learning and their application in data
science. He has published more than 15 papers, mostly in the field of image processing and data
analysis.
